Documentation for Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors, Version 3

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Detailed Data Description

Format

Daily Gridded and Mean Gridded Files

The daily gridded files provide ice motion vectors by merging the raw ice motion vectors together using a set of rules. See the Processing Steps section of this document for more information on this merging process.

The mean gridded files provide averages of the daily gridded data at different resolutions: weeks and months and the complete time series. Both the daily and mean gridded fields are projected to Northern and Southern Hemisphere EASE-Grids. Data are stored in 2-byte integer binary format (little endian) and are pixel-interleaved three-item vectors (u, v, 3). Each vector represents three variables:

  • U Component (cm/sec)—Scaled by a factor of 10; divide by 10 to revert to original units.
  • V Component (cm/sec)—Scaled by a factor of 10; divide by 10 to revert to original units.
  • Third Variable—Varies for daily and mean grids (see below). For both daily and mean grids, a pixel value of 0 in the third variable indicates no vectors at that location.
Third Variable for Daily Grids

For the daily grids, the third variable contains the square root of the estimated error variance, scaled by a factor of 10, at a given location. The error variance is the estimated error of that vector obtained from the optimal interpolation process. The input vectors from the individual sources (NCEP/NCAR Winds, SSM/I, SSMIS, SMMR, AMSR-E, and AVHRR) are weighted separately based upon cross-correlations with buoy vectors. The optimal interpolation uses these weights, along with their distances from the location being estimated, to obtain the final error variance.

If the closest input vector was greater than 1250 km, then a value of 1000 is added to this variable. Because interpolation was applied to a surface map from passive microwave data, coastlines may contain false ice. In this case, the third variable was assigned a negative value to allow users to remove these vectors near coastlines (within 25 km). For example, a value of -1035 indicates all of the following conditions:

  • The vector was near a coastline
  • The nearest sampled vector was further than 1250 km
  • The vector had a σ value of 3.5, or the estimated error variance (σ2) is 12.25
Third Variable for Mean Grids

For the mean grids, the third variable is the number of daily gridded values that contributed to the mean value. For example, at a grid point in the weekly product, the number of vectors would be between 1 and 7, indicating the number of days of the week with a valid vector at that grid point; for a monthly product, the number would be between 1 and 31. Generally, the greater fraction of days in the mean field that contain valid values, the higher the data quality.

Thus, the information contained in the third variable provides a means of characterizing data quality, in addition to the "near coastline" check described above. For example, a data user might choose to filter out vectors with error variances above a certain level or values for which the nearest observed vector was beyond a particular distance.

Raw Ice Motion Vector Files

The raw ice motion vector files provide the motion vectors from each specific sensor in space-delimited ASCII text format. Each daily file contains a variable number of vectors that are described at the top of every file in a one-line header containing three numbers as described in Table 1.

Table 1. Header Row Description for Raw Ice Motion Vector Files
Number Description
First Specifies the number of vectors (lines) in the file
Second Original grid dimensions (x)
Third Original grid dimensions (y)

After the header line, the data are listed in five columns for all files, except for the IABP buoy data which contains six columns. The columns are described in Table 2.

Table 2. Column Descriptions for Raw Ice Motion Vector Files
Column Name Description
1 x EASE-Grid row number for the start of the vector (vector starts in the center of the grid cell). The upper left corner is represented by x = -0.5
2 y EASE-Grid column number for the start of the vector (vector starts in the center of the grid cell). The upper left corner is represented by y = -0.5
3 u The horizontal vector component in cm/sec
4 v The vertical vector component in cm/sec
51 z

Source of the data; z value varies depending on instrument:

AMSR-E: z = 0.0 – 1.0 (correlation coefficient); only 89V GHz channel used.
AVHRR: z = Number of vectors averaged together at a given location from up to four passes and two channels (thermal and visible).
Buoys: z = IABP buoy number
SMMR: z = 1: The vector was from 37V GHz channel
z = 2: The vector was from both 37 GHz channels
SSM/I and SSMIS: z = 1: The vector was from 37V GHz channel
z = 2: The vector was from both 37 GHz channels
z = 3: The vector was derived from the 85V GHz channel
Winds: z = 1: From NCEP/NCAR wind data
1 For the buoy data, there are six columns with the fifth column containing time of day in Universal Coordinated Time (UTC) and the sixth column containing the z value with the IABP buoy number.

See the Sample Data Record for an example of a raw vector file derived from SSM/I.

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File and Directory Structure

Data are available on the HTTPS site at: https://daacdata.apps.nsidc.org/pub/DATASETS/nsidc0116_icemotion_vectors_v3/

There are three main subdirectories: browse, data, and tools. Within these subdirectories, folders are subdivided. Table 3 provides a listing of the subfolders.

Table 3. Data and Browse Directory Structure
Data Subdirectory
Description
/north/amsre/ Contains the AMSR-E raw daily ice motion vector files available for only the northern hemisphere. Within this directory, the data are divided by year.
/north/avhrr/
/south/avhrr/		 Contains the AVHRR raw daily ice motion vectors. Within this directory, the data are divided by year.
/north/buoy/ Contains the IABP buoy raw daily ice motion vectors available for only the northern hemisphere. Within this directory, the data are divided by year.
/north/grid/
/south/grid/		 Contains the merged gridded daily ice motion vectors. Within this directory, the data are divided by year.
/north/means/
/south/means/		 Contains the monthly mean gridded ice motion vectors. Within this directory, the data are divided by year.
/north/monthly_clim
/south/monthly_clim		 Contains calendar month averages. For example, the file with "01" in it is the average motion of all the January files in the data record.
/north/smmr/
/south/smmr		 Contains the SMMR raw daily ice motion vector files for the SSM/I fleet of instruments. Within this directory, the data are divided by year.
/north/ssmi/
/south/ssmi		 Contains the SSM/I and SSMIS raw daily ice motion vector files. Within this directory, the data are divided by year.
/north/week/
/south/week/		 Contains seven day averages of the ice motions.
/north/wind/ Contains the NCEP/NCAR raw daily wind vector files. Within this directory, the data are divided by year.
Browse Subdirectory
Description
/north/month/
/south/month/		 Contains the browse images of all of the monthly mean fields. Within this directory, the data are divided by year.
/north/monthly_clim/
/south/monthly_clim/		 Contains calendar month averages. For example, the file with "01" in it is the average motion of all the January files in the data record.
/north/week/
/south/week/		 Contains the browse images of all of the weekly mean fields. Within this directory, the data are divided by year.
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File Naming Convention

Raw Ice Motion Vector Data Files

This section explains the raw ice motion vector data file naming convention used for this product with an example.

Example File Name:
icemotion.vect.amsre.2002170.n.v3.txt
icemotion.vect.xxxx.yyyyddd.h.vVV.ext

Refer to Table 4 for the valid values for the file name variables listed above.

Table 4. Ice Motion File Naming Convention
Variable
Description
icemotion.vect Indicates that the file contains ice motion vectors
xxxx Sensor File (amsre1, avhrr, buoy1, ssmi, or smmr2) wind
yyyy 4-digit year3
ddd 3-digit day of year3
h Hemisphere (n: Northern, s: Southern)
vVV Version (v3)
.ext File extension (.txt: ASCII text file)
1 Available for Northern Hemisphere only. 
2 Files named ssmi include two sensors: SSM/I and SSMIS.
3 File dates indicate the beginning of the vector, either the start of buoy motion or the first satellite image.

Mean Gridded Data Files

This section explains the mean gridded data file naming convention used for this product with an example.

Example File Names:
icemotion.grid.daily.1978298.n.v3.bin
icemotion.grid.xxx.yyyy.ddd.h.vVV.ext

icemotion.grid.week.1978.44.n.v3
icemotion.grid.xxx.yyyy.ww.h.vVV.ext

icemotion.grid.month.1978.11.n.v3
icemotion.grid.xxx.yyyy.mm.h.vVV.ext

icemotion.grid.monthlyclim.01.n.v3 
icemotion.grid.xxx.mm.h.vVV.ext

Refer to Table 5 for the valid values for the file name variables listed above.

Table 5. File Naming Convention Values
Variable
Description
icemotion.grid Indicates that the file contains mean gridded data
xxx Indicates the type of file: daily, week, month, or monthlyclim
yyyy 4-digit year
ddd 3-digit day of year
ww 2-digit week of year
mm 2-digit month of year
h Hemisphere (n: Northern, s: Southern)
vVV Version (v3)
.ext File extension (.bin: binary data file, .png: PNG browse image)

Browse Files

This section explains the browse file naming convention used for this product with an example.

Example File Names
icemotion.browse.month.1978.11.n.v3.png
icemotion.browse.xxx.yyyy.mm.h.vVV.ext

icemotion.browse.week.1978.45.n.v3.png
icemotion.browse.xxx.yyyy.ww.h.vVV.ext 

icemotion.browse.monthlyclim.01.n.v3.png
icemotion.browse.monthlyclim.mm.h.vVV.ext

Refer to Table 6 for the valid values for the file name variables listed above.

Table 6. File Naming Convention Values
Variable
Description
icemotion.browse Indicates that the file contains browse images.
xxx Indicates the type of image: week, month, or monthlyclim
yyyy 4-digit year
ww 2-digit week of year
mm 2-digit month of year
h Hemisphere (n: Northern, s: Southern)
vVV Version (v3)
.ext File extension (.png: PNG browse image)
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File Size

Approximate file sizes are as follows:

  • Raw ice motion vectors (.txt): 14 KB - 319 KB
  • Daily and mean grids (.bin): 604 KB (Southern Hemisphere), 764 KB (Northern Hemisphere)
  • Browse images (.png) 1.5 MB
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Spatial Coverage

Tables 7 and 8 list the values of corner grid cells for the Northern and Southern Hemispheres, respectively.

Table 7. Northern Hemisphere Pixels
Corner
Center of Pixel
Outer Edge of Pixel
Upper Left 29.89694° N, 135.00000° W 29.71270° N, 135.00000° W
Upper Right 29.89694° N, 135.00000° E 29.71270° N, 135.00000° E
Lower Left 29.89694° N, 45.00000° W 29.71270° N, 45.00000° W
Lower Right 29.89694° N, 45.00000° E 29.71270° N, 45.00000° E
Table 8. Southern Hemisphere Pixels
Corner
Center of Pixel
Outer Edge of Pixel
Upper Left 37.13584° S, 45.00000° W 36.95776° S, 45.00000° W
Upper Right 37.13584° S, 45.00000° E 36.95776° S, 45.00000° E
Lower Left 37.13584° S, 135.00000° W 36.95776° S, 135.00000° W
Lower Right 37.13584° S, 135.00000° E 36.95776° S, 135.00000° E
Spatial Coverage Maps
Figure 1a. Spatial Coverage Map for the Northern Hemisphere

Figure 1b. Spatial Coverage Map for the Southern Hemisphere
Determining Vector Components

Note that the U and V vector components are determined with respect to the grid; positive U vectors run from left to right and positive V vectors run from bottom to top. Thus, consider the longitude when retrieving East/West and North/South components.

Spatial Resolution

Source data are regridded to Northern and Southern Hemisphere EASE-Grids with 25 km pixel spacing.

Projection and Grid Description

Data are georeferenced to the EASE-Grid projection, an azimuthal equal area projection. The northern grid is 361 x 361, centered on the geographic North Pole. The southern grid is 321 x 321, centered on the geographic South Pole. Nominal grid size is 25 km. Grid coordinates begin in the center of the upper left grid cell. These grids are subsets of the Northern and Southern EASE-Grids.

Further details on the EASE-Grid projection are provided on the Original EASE-Grid Format Description web page. For more information on related products and tools, refer to the EASE-Grid website.

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Temporal Coverage

The temporal coverage and resolution vary by type of data and/or by sensor, as shown in Table 9.

Table 9. Temporal Coverage and Resolution
Data
Type/Sensor
Start Date
End Date
Resolution
Daily Gridded Fields Daily 31 October 1978 28 February 2017 Daily
Daily ASCII Ice Motion Vectors
AMSR-E 19 June 2002 08 August 2011 Data are available every day for any given grid cell.
AVHRR 24 July 1981 31 December 2004 Four satellite passes are used each day when available.
Buoys 18 January 1979 28 February 2017 The 12:00 Greenwich Mean Time (GMT) buoy positions
were used to compute 24-hour mean velocities.
NCEP/NCAR (Winds) 01 November 1978 28 February 2017 Data are available every day for any given grid cell.
SMMR 25 October 1978 08 July 1987 Data are available every other day for any given grid cell. See the SMMR 48 Hour Temporal Resolution Consequences for more information.
SSM/I 09 July 1987 31 December 2006 Data are available every day for any given grid cell.
SSMIS 01 January 2007 28 February 2017 Data are available every day for any given grid cell.
Mean Gridded Fields
Single Months November 1978 February 2017 Monthly
Weekly Means Week 45 in 1978 Week 8 in 2017 Weekly

Buoy Data Removed

In Version 3, buoy ice motion estimates greater than 70 cm/s over a 24 hour period were deemed to be physically unrealistic; thus, buoy velocities that exceed this threshold were excluded from this data set.

Ice Motion Missing Data

In Version 3, there are some missing days of data in the Southern Hemisphere because there was not enough data from SSMI or AVHRR to yield ice motion vectors. There are a total of 13164 days of data. We have 12992 days of that data, with 172 days of missing data. Refer to Table 10 for the days of the missing data.

Table 10. Days of Missing Ice Motion Data
Year
Missing Days of Data
1979
 12, 13, 20-25, 28, 29, 48, 49, 60, 61 354, 355, 364
1980
 1, 2 11-16, 19-22, 25-28, 31-36, 39-46, 67, 68, 73-78, 87, 88
1981
 15 16 23 24 27 28 31 32 39 40 51 52 61 62 69 70
1982
 28-32, 36, 54-57, 215-217
1983
 27 28 44 57
1984
 1 14 15 18 26 27 38 39 80 81 82 235 236 237
1985
 15 36 37
1986
 87 89 337 350
1987
 18 19 24 25 26 27 357 360 361 362
1988
 37 59 60
1989
 22 23 48 66 72 73 137 139 361 362 364
1990
 18 33 35 42 46 48 49 51 53 68 78 93
1991
 53 68 75
1992
 7 15 25 38 44
1993
 69
1994
 323 324 325
1996
 335 336
1997
 29 34
2000
 36
2004
 6
2005
 33
2010
 30 32 33 178 179
2011
 20

AVHRR Data Removed

It was discovered that previous versions of AVHRR motion vector data files had motion estimates based on comparison of AVHRR images that were misregistered relative to each other. Thus, some AVHRR motion estimates were removed from Version 3 data. Refer to Table 11 for the dates of the data that were removed.

Table 11. Days of Missing AVHRR Data
Hemisphere
Year
Missing Days of Data
Northern 1993 258
1996 225 226
Southern 1981 355
1983 174, 206-208, 241-242, 262-270, 306, 329, 343
1984 332 333
1985 97
1987 250
1988 121
1989 50
1994 16
1995 7 18
1996 137 138
2000 72 282
 
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Sample Data Record

Following is a sample of raw vectors derived from SSM/I data. The first ten lines of icemotion_vect_ssmi_2003078_n_v3.txt are shown in Figure 2. The first line is the header and indicates that this file contains 1267 vectors and that the original grid was 1805 x 1805 pixels. For a description of the data columns, see the Raw Ice Motion Vectors Format section of this document. Figure 3 shows a browse image for the monthly mean ice motion for January 2014.

1267 1805 1805
    792.50    267.50      7.23     16.27      3.00
    822.50    267.50      5.43     21.70      3.00
    807.50    282.50      7.24     14.47      3.00
    852.50    282.50      7.24     14.47      3.00
    867.50    282.50      5.43      9.04      3.00
   1242.50    282.50      0.00     -0.00      3.00
    837.50    297.50      3.62     14.47      3.00
    852.50    297.50      5.43     16.27      3.00
    867.50    297.50      3.62     10.85      3.00

Figure 2. First ten lines of file icemotion.vect.ssmi.2003078.n.v3.txt. 

Northern Hemisphere Spatial Coverage
Figure 3. Monthly mean ice motion for January 2015. 

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Quality Assessment

The Measuring Sea Ice Motion web page provides accuracy estimates of ice motion from each sensor:

  • Ice motion from AVHRR
  • Ice Motion from Passive Microwave: SMMR, SSM/I, SSMIS, and AMSR-E
  • Ice motion from IABP Buoys
  • Ice motion from NCEP/NCAR Winds
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Software and Tools

Table 12. Tools
Tool
Description
uninterleave.c
README.sequential		 ImageJ is a visualization tool used to examine raw binary data files. While ImageJ can handle multiple images in a single data file, these images must be sequential rather than interleaved. The binary gridded ice motion files may be converted from interleaved to sequential files using the C program uninterleave.c available via the FTP directory in the tools folder. Further instructions on running uninterleave and using ImageJ can be found in the README.sequential file in the FTP directory.

show_vectors_v3.pro
nsidc_north_map
nsidc_south_map

A Read/Plot Data IDL program is available via the FTP directory in the tools folder that can read ice motion data and create PostScript plots or display data to a screen.The file show_vectors_v3.pro displays AMSR-E, AVHRR, SMMR, SSM/I, SSMIS, and buoy-derived ASCII sea ice motion vectors. The map files nsidc_north_map and nsidc_south_map must be in the same directory as the IDL program.

The following is an example of running an IDL program using the vector file for SSM/I and SSMIS:
IDL> show_vectors_v3, 'icemotion_vect_ssmi_2012365'

disp_ice_motion_v3.pro
nsidc_north_map 
nsidc_south_map

An Animate Data IDL program is available via the FTP directory in the tools folder that animates raw data and daily and mean gridded data by day, week, month, or year. The file disp_ice_motion_v3.pro displays ice motion vectors. The map files nsidc_north_map and nsidc_south_map must be in the same directory as the IDL program.

To run this code, open IDL and type the name of the routine. For example:

IDL> disp_ice_motion_v3
% Compiled module: DISP_ICE_MOTION.
north_x_y_lat_lon
south_x_y_lat_lon

In addition, the tools directory contains the following latitude and longitude grids with 25 km pixel spacing, which provide row/column to latitude/longitude conversion information: 
north_x_y_lat_lon
south_x_y_lat_lon

Table 13 provides the descriptions for the four columns in each of these files.

Table 13. Column Descriptions for Latitude and Longitude Files
Column
Name
Description
1 x Grid row number
2 y Grid column number
3 lat Corresponding latitude
4 lon Corresponding longitude
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Data Acquisition and Processing

Theory of Measurements

Sea ice movement is measured using imagery acquired by frequent, repeat coverage of remote sensing instruments. Ice motion computed from satellite imagery represents the displacement between the acquisition times of two images with the same spatial coverage. Researchers identify a feature, such as an ice floe, on two registered images and measure its pixel displacement. Ice velocity vectors are computed based on the pixel resolution and time span between images.

A more automated method is to measure the correlation of groups of pixels between image pairs. A small target area in one image is correlated with several areas of the same size in a search region of the second image. The displacement of the ice is then defined by the location in the second image where the correlation coefficient is the highest. This spatial correlation method is used to produce ice motion vectors for this data set. This approach is generally valid over short distances away from the ice edge in areas where ice conditions are relatively stable from day to day. Spatial correlation methods cannot, however, find matches between images where a complete knowledge of ice dynamics is needed; for example, in areas where ice is deforming or in the ice margins near the open ocean where the spatial or spectral characteristics of the ice within a pixel are changing rapidly (Emery, Fowler, and Maslanik 1995).

Ice Motion Estimates Where No Ice Exists

The passive microwave ice motion estimates are based on changes in brightness temperatures over consecutive days. We used ice concentration estimates greater than 15 percent from the Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data Set, NSIDC-0051, to indicate where ice extent is present.

The methods used to generate these ice motions at current resolutions requre fairly large areas of ocean. For instance, no motions can be calculated in the Canadian Archipelago. The absence of ice motion estimates in such locations does not imply the absence of ice in these locations.

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Data Sources

AVHRR Data

AVHRR Global Area Coverage (GAC) images at a 5 km gridded resolution were used to estimate ice motion over the Arctic and Antarctic for several reasons. First, they were available for nearly two decades. Second, they provide an intermediate spatial resolution between passive microwave and buoys and finer time sampling than microwave data. Finally, they are not subject to the same error sources as the other data sets. AVHRR channel 2 (visible band) and channel 4 (infrared) are used.

Buoy Data

International Arctic Buoy Program (IABP) C buoy position data were used to calculate ice motion vectors from buoys. IABP provides buoy location information through satellite tracking of buoys placed on sea ice. Several buoy locations are determined each day and corresponding ice motions are calculated. Ice motion from buoys is very accurate, but it is limited since the numbers and locations of buoys are driven by cost and logistics. In addition, buoys have not been placed on ice in the Eastern Arctic.

IABP buoy locations are generally provided every 12 hours: at noon and at midnight Greenwich time. This ice motion product uses 24-hour motion estimates from the IABP. For example, the IABP motion estimate for a buoy at noon on 01 January 2010 is derived by taking the difference of the buoy's location at noon on 02 January 2010 and its location at noon on 01 January 2010 and then dividing by 24 hours. The intervening midnight location value is not factored into the noon-to-noon 24-hour motion estimate. Similarly, the IABP motion estimate for midnight is calculated the same way, ignoring the intervening noon location information. Therefore, each buoy generally has two independent 24-hour motion estimates, one for midnight and one for noon.

NCEP/NCAR Data

NCEP/NCAR Reanalysis data were used to derive wind vectors for this data set. The data, called U-wind at 10 m, are available from the NOAA Earth System Research Laboratory (ESRL) Physical Sciences Division (PSD).

The NCEP/NCAR Reanalysis source data set is an assimilation of land surface, rawinsonde, ship, pibal, aircraft, satellite, and various other data within a global weather model. A partial list of some of the sensors and data sources used in the NCEP/NCAR Reanalysis is provided in Table 14. For complete documentation regarding the sensors used as a basis for the NCEP/NCAR data, refer to the NCEP-NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation paper (Kistler 2001).

Table 14. Examples of Data Sources/Sensors used in NCEP/NCAR Reanalysis Data
Example Data Type
Example Data Source and/or Sensor(s)
Description
Rawinsonde NCEP Global Telecommunication System (GTS) data The main source for the rawinsonde data, a global collection of upper-air observation data. Also includes pibal and aircraft data.
Surface Marine Data Comprehensive Ocean-Atmosphere Data Set (COADS) data Among other data, includes data from ships, drifting buoys, fixed buoys, pack-ice buoys, and near-surface data from ocean station reports, such as Expendable Bathythermographs (XBTs).
Aircraft Data NCEP Global Telecommunication System (GTS) data The main source for the aircraft data, a global collection of upper-air observation data. Also includes pibal and rawinsonde data.
Surface Land Synoptic Data Air Force Global Telecommunication System (GTS) data The main source for the surface land synoptic data, a global collection of surface data.
Satellite Sounder Data TIROS Operational Vertical Sounder (TOVS) sensors: 
High Resolution Infrared Radiation Sounder (HIRS)
Microwave Sounding Unit (MSU) 
Stratospheric Sounding Unit (SSU)		 The TOVS suite of sensors provides global measurements used in weather forecasting, such as the vertical distribution of temperature and moisture in the atmosphere.
Surface Wind Speed Data Special Sensor Microwave Imager (SSM/I) SSM/I data were used with the Krasnopolsky et al. (1995) algorithm which resulted in wind speeds closer to buoy data, and coverage under cloudy conditions. Measurements include SSM/I wind speed, total precipitable water, and other parameters. (Kalnay et al. 1996)
Satellite Cloud Drift Wind Data Geostationary Meteorological Satellite (GMS) data The GMS program is a series of satellites operated by the Japan Meteorological Agency (JMA). The Visible and Infrared Spin Scan Radiometer (VISSR), the primary instrument aboard GMS, collects visible and infrared images of Earth and its cloud cover.

Passive Microwave Data

Passive microwave data come from four different instruments: SMMR, SSM/I, SSMIS, and AMSR-E. All the data are downloaded from NSIDC. The SMMR data comes from the NIMBUS-7 SMMR Pathfinder Brightness Temperatures data set at a 25 km gridded resolution. Due to satellite limitations, full Arctic coverage is only available every two days with SMMR. See the SMMR 48 Hour Temporal Resolution Consequences discussion for more information.

The SSM/I and SSMIS data come from DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures and the AMSR-E data come from the AMSR-E/Aqua L2A Global Swath Spatially-Resampled Brightness Temperatures. These passive microwave data essentially provide all-sky coverage, whereas AVHRR visible and infrared data are limited by cloud cover. Table 15 provides the channels used from each instrument and their frequencies and resolutions.

Table 15. Channel Frequencies and Resolutions of the Passive Microwave Instruments
Instrument
Channel Frequency
Resolution
AMSR-E 89 GHz vertical 12.5 km
SMMR 37 GHz vertical and horizontal 25 km
SSM/I 37 GHz vertical and horizontal
85 GHz vertical and horizontal		 25 km
12.5 km
SSMIS 37 GHz vertical and horizontal
91 GHz vertical and horizontal		 25 km
12.5 km
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Processing Steps

The following steps were used to create this ice motion product.

  1. Grid the Input Data to the 25 km EASE-Grid
     
  2. Compute the Ice Motion Fields
    Detailed information about the methods used to compute ice motion fields is available on the Measuring Sea Ice Motion web page.
     
  3. Merge the Ice Motion Fields
    ​Each of the ice motion estimates, for example from NCEP winds, IABP buoys, or AMSR-E, are computed and gridded individually. Once computed, these independent estimates are then combined into a final motion estimate. Each source is weighted according to the expected accuracy of the source data. For example, estimates derived from nearby buoys are weighted higher than NCEP-derived estimates. 

    To compute the final gridded motion estimate, each of the independent estimates are mapped to the output grid. A source-weighted and distance-weighted average of the nearest 15 estimates is used to compute the final motion estimate. Note that where data are sparse, the data sources will be widely separated; and when data are dense, only the very nearest estimates are considered. If the motion estimates vary significantly from each other, this method can result in motion fields that do not always vary smoothly.

    See the Merged Daily Gridded Vectors for more information. 
     
  4. Compute Mean Fields
    Mean ice motion was computed from the merged daily gridded ice motion data. The northern and southern polar regions have two mean fields: weekly and monthly.

    For the weekly means, at least five out of seven days were needed to compute each vector mean. Weekly means for each year start at 01 January for consistency. The last day of each year (or last two days if in a leap year) were not used. For example, week 1 is always 1-7 January and week 52 is either 24-30 December or 23-29 December, if in a leap year.

    For the monthly means, at least 20 days were needed. For any mean greater than one month, at least 40 days were needed.
     
  5. Write Data to ASCII and Binary Data Files
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Processing History

Table 16 outlines the processing and algorithm history for this product.

Table 16. Description of Processing Changes
Version
Date
Description of Changes
V3 February 2016
  • Eliminated unrealistic AVHRR and IABP buoy velocities
  • Extended buoy ice motion estimates to the present
  • Improved browse images
  • Reprocessed SSMI fields using GDAL map transformations on the DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures Data Set, NSIDC-0001.
  • Used Ice concentration estimates greater than 15 percent from the Sea Ice Concentrations from Nim bus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data Set, NSIDC-0051,to indicate where ice extent is present.
V2 Sep 2013
  • Added AMSR-E and NCEP/NCAR data for the Northern Hemisphere
V1 May 2003 Original version of data. Note: V1 is not indicated in Version 1 file names
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Sensor or Instrument Description

Refer to the following for information on each sensor:

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References and Related Publications

Cavalieri, D. J., C. L. Parkinson, P. Gloersen, J. C. Comiso, and H. J. Zwally. 1999. Deriving long-term time series of sea ice cover from satellite passive-microwave multisensor data sets, J. Geophys. Res., 104(C7), 15803-15814, doi:10.1029/1999JC900081.

Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally. 1996, updated yearly. Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave Data, Version 1. [indicate subset used]. Boulder, Colorado USA: NASA DAAC at the National Snow and Ice Data Center. http://dx.doi.org/10.5067/8GQ8LZQVL0VL.

Cracknell, A. 1997. The Advanced Very High Resolution Radiometer. London: Taylor and Francis.

Emery, W., C. Fowler, and J. Maslanik. 1995. Satellite Remote Sensing of Ice Motion, in Oceanographic Applications of Remote Sensing, ed. Motoyoshi Ikeda and Frederic W. Dobson. CRC Press, Boca Raton.

Haumann, F. Alexander, Nicolas Gruber, Matthias Münnich, Ivy Frenger and Stefan Kern. 2016. Sea-ice transport driving Southern Ocean salinity and its recent trends. Nature 537:89-92. http://dx.doi.org/10.1038/nature19101.

Isaaks, E., and R. M. Srivastava. 1989. An Introduction to Applied Geostatistics. New York: Oxford University Press.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, A. Leetmaa, R. Reynolds, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. Mo, C. Ropelewski, J. Wang, R. Jenne, and D. Joseph. 1996. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77, 437–471.

Kidwell, K. 1995. NOAA Polar Orbiter Data User's Guide. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, NESDIS.

Kistler, R., et al. 2001. The NCEP–NCAR 50–Year Reanalysis: Monthly Means CD-ROM and Documentation. Bull. Amer. Meteor. Soc., 82, 247-267. doi: http://dx.doi.org/10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2.

Krasnopolsky, V. M., L. C. Breaker, and W. H. Gemmill. 1995. A Neural Network as a Nonlinear Transfer Function Model for Retrieving Surface Wind Speeds from the Special Sensor Microwave Imager. Journal of Geophysical Research, 100(C6), 11,033–11,045. PDF

Maslanik, J., C. Fowler, J. Key, T. Scambos, T. Hutchinson, and W. Emery. 1998. AVHRR-based Polar Pathfinder Products for Modeling Applications. Annals of Glaciology 25:388-392

Rosborough, G., D. Baldwin, and W. Emery. 1994. Precise AVHRR Image Navigation. IEEE Transactions in Geosciences and Remote Sensing 32(3):644-657.

Schweiger, A., C. Fowler, J. Key, J. Maslanik, J. Francis, R. Armstrong, M. J. Brodzik, T. Scambos, T. Haran, M. Ortmeyer, S. Khalsa, D. Rothrock, and R. Weaver. 1999. P-Cube: A Multisensor Data Set for Polar Climate Research. Proceedings on the 5th Conference on Polar Meteorology and Oceanography, American Meteorological Society, Dallas, TX, 15-20 Jan., 136-141.

Thorndike, A. S., and R. Colony. 1982. Sea Ice Motion in Response to Geostrophic Winds. J. Geophys. Res. 87(C8):5845–5852, doi:10.1029/JC087iC08p05845.

RELATED DATA COLLECTIONS

RELATED WEBSITES

Contacts and Acknowledgments

Mark Tschudi
University of Colorado
Colorado Center for Astrodynamics Research (CCAR)
431 UCB
Boulder, Colorado USA 80309-0431

Chuck Fowler (retired)
University of Colorado Boulder
Boulder, CO 80309

Jim Maslanik (retired)
University of Colorado Boulder
Boulder, CO 80309

J. Scott Stewart
University of Colorado Boulder
449 UCB
Boulder, CO 80309-0449

Walt Meier
NASA Goddard Space Flight Center (GSFC)
Mail Code: 615
Greenbelt, MD 20771

Document Information

DOCUMENT CREATION DATE

May 2003

DOCUMENT REVISION DATE

March 2016
February 2015
September 2013

Appendix - Merged Daily Gridded Vectors

Note: The following appendix was produced to support Version 2 of this data set.

Methods

Optimal interpolation was used to create daily gridded ice motion fields from SMMR, SSM/I-SSMIS, AMSR-E, AVHRR, IABP buoy data, and NCEP/NCAR wind data. A cokriging estimation method described by Isaaks and Srivastava (1989) was employed for the interpolation. This method utilizes the cross-correlation between several variables—in this case, the vectors derived from SMMR, SSM/I-SSMIS,AMSR-E, AVHRR, IABP buoy data, and NCEP/NCAR winds—to minimize the estimation error variance. See Figure A1 for more details.

Figure A1. Example of autocorrelation (jagged line) for the u component of the vectors derived from AVHRR


AVHRR autocorrelation

The plot shows discontinuity near 50 km distance. If discontinuity is ignored, the autocorrelation line would cross the zero distance line at about 0.8. This value, which is related to measurement error, is often referred to as the "nugget effect" in geostatistics. The shape of the curve is similar for both u and v vector components and for all of the auto-correlation and cross-correlation functions among all of the data sets. The major difference is the starting point at zero km distance. The autocorrelations for the ice motions for AMSR-E were very similar to AVHRR, and the vectors from the wind data were very close to the 37 GHz vectors. See Table A1 for more details. 

Table A1. Starting points for each of the auto- and cross-correlations
BUOY AVHRR/AMSR-E SSM/I-SSMIS 85 GHz SMMR or SSM/I-SSMIS 37 GHz/Winds
BUOY .95 .70 .70 .40
AVHRR/AMSR-E .85 .65 .30
85 GHz .80 .40
37 GHz/Winds .45

A simple exponential function was used to model the correlation (dotted line) in the above figure. This function provided the best model to approximate the vector data. Cross-correlations between the u and v components were performed over the entire Arctic and Antarctic regions, and it was found that they were nearly independent. The u and v components were thus treated independently and were interpolated separately for simplicity. Using the modeled correlation functions, vectors for each ice-covered area on the 25 km EASE-Grid werre interpolated. See Figure A2 for an example.

Figure A2. Vector plot for 20 April 2000, where every fourth vector is plotted


Sample daily-averaged vector plot

The number of vectors from the buoy, passive microwave, and AVHRR data varies. A plot of monthly average percent coverage of ice-covered areas shows that summer weather conditions affect the ability of satellites to track ice. Figure A3 shows the percentage of sea ice grid cells that contain vectors.

Figure A3. Percent coverage of sea ice grid cells

Figure A4 shows the average number of vectors available from each source.

Figure A4. Number of vectors from each source

Similarly, Figure A5 shows the percentage of available vectors from each source.

Figure A5. Percentage of vectors from each source

Note that the vectors from the 85 GHz channels began in 1987 with the launch of the SSM/I instrument.

Final Summary

Gridded vector fields are not the optimal data set for everyone. They are best used for evaluating climatological patterns and changes in sea ice motion over the last 25 years. Various assumptions were factored into the production of gridded vector fields. For optimal interpolation, the data should be stationary, homogenous, and isotropic. Sea ice does not have these characteristics. Changes in ice concentration and thickness may negate the assumptions. In an ideal case, two-dimensional correlation functions are found at every grid location on each day to help alleviate some of these problems.

Another limitation of the data is cloud cover, which can affect a satellite's ability to discern sea ice, and thus, the number and location of available vectors on each day.

Data users should use the summer vector plots with caution due to the low number of vectors resulting from surface melt and increased cloud cover in the summer. As individual summer daily plots contain significant noise, weekly- and monthly-mean plots are more reliable.

Accuracy

Several years of vectors were interpolated to the same grid for comparison, but without using the buoy data. The mean difference between the interpolated u components and the buoy vectors was 0.1 cm/sec with a Root Mean Square (RMS) error of 3.36 cm/sec. For the v component, the mean was 0.4 cm/sec with an RMS error of 3.40 cm/sec.